Titlebook: Computer Games; 6th Workshop, CGW 20 Tristan Cazenave,Mark H.M. Winands,Abdallah Saffid Conference proceedings 2018 Springer International

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Applying Anytime Heuristic Search to Cost-Optimal HTN Planning,nch-and-bound with a heuristic search, which can also be used as a near-optimal algorithm given a time limit. It also includes different heuristics based on weighted cost estimations and different decomposition strategies. The different elements from this framework are empirically evaluated on three

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A Game for Eliciting Trust Between People and Devices Under Diverse Performance Conditions,etirement village, where residents live in smart homes equipped with monitoring systems. Players, who “work” in the village, need to trade-off the time spent on administrative tasks (which enable them to earn extra income) against the time spent ensuring the welfare of the residents. The scenario of

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Disorders of the Back and Neck,m exceptionally suitable for CPU computations. However, given the fact that deep reinforcement learning often deals with interpreting visual information, a large part of the train and inference time is spent performing convolutions..In this work we present our results on learning strategies in Atari

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https://doi.org/10.1007/978-3-662-59752-1ing direct evaluation of the current state and actions. This is in stark contrast to the algorithms for traditional board games such as Chess or Go, where a look-ahead search mechanism is indispensable to build a strong agent. In this paper, we present a novel deep reinforcement learning architectur

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